Publisher Summary The chapter has described the measurement system and the procedure followed for the computation of the fluxes and the procedure of flux summation, including data gap filling strategy, night flux corrections and error estimation. It begins with the introduction of estimates of the annual net carbon and water exchange of forests using the EUROFLUX methodology. The chapter then provides us with the theory and moves on to discuss the eddy covariance system and its sonic anemometer, temperature fluctuation measurements, infrared gas analyser, air transport system, and tower instrumentation. Additional measurements are also given in the chapter. Data acquisition and its computation and correction is discussed next in the chapter by giving its general procedure, half-hourly means (co-)variances and uncorrected fluxes, intercomparison of software, and correction for frequency response losses. The chapter has also discussed about quality control and four criteria are investigated here for the same. Spatial representativeness of measured fluxes and summation procedure are reviewed. The chapter then moves on to the discussion of data gap filling through interpolation and parameterization and neural networks. Corrections to night-time data and error estimation are also explored in the chapter. Finally, the chapter closes with conclusions.

Estimates of the annual net carbon and water exchange of forests: the EUROFLUX methodology

Deep-learning (DL) algorithms, which learn the representative and discriminative features in a hierarchical manner from the data, have recently become a hotspot in the machine-learning area and have been introduced into the geoscience and remote sensing (RS) community for RS big data analysis. Considering the low-level features (e.g., spectral and texture) as the bottom level, the output feature representation from the top level of the network can be directly fed into a subsequent classifier for pixel-based classification. As a matter of fact, by carefully addressing the practical demands in RS applications and designing the input?output levels of the whole network, we have found that DL is actually everywhere in RS data analysis: from the traditional topics of image preprocessing, pixel-based classification, and target recognition, to the recent challenging tasks of high-level semantic feature extraction and RS scene understanding.

Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art

Deep Convolutional Neural Network (CNN) is a special type of Neural Networks, which has shown exemplary performance on several competitions related to Computer Vision and Image Processing. Some of the exciting application areas of CNN include Image Classification and Segmentation, Object Detection, Video Processing, Natural Language Processing, and Speech Recognition. The powerful learning ability of deep CNN is primarily due to the use of multiple feature extraction stages that can automatically learn representations from the data. The availability of a large amount of data and improvement in the hardware technology has accelerated the research in CNNs, and recently interesting deep CNN architectures have been reported. Several inspiring ideas to bring advancements in CNNs have been explored, such as the use of different activation and loss functions, parameter optimization, regularization, and architectural innovations. However, the significant improvement in the representational capacity of the deep CNN is achieved through architectural innovations. Notably, the ideas of exploiting spatial and channel information, depth and width of architecture, and multi-path information processing have gained substantial attention. Similarly, the idea of using a block of layers as a structural unit is also gaining popularity. This survey thus focuses on the intrinsic taxonomy present in the recently reported deep CNN architectures and, consequently, classifies the recent innovations in CNN architectures into seven different categories. These seven categories are based on spatial exploitation, depth, multi-path, width, feature-map exploitation, channel boosting, and attention. Additionally, the elementary understanding of CNN components, current challenges, and applications of CNN are also provided.

https://link.springer.com/content/pdf/10.1007/s10462-020-09825-6.pdf

A survey of the recent architectures of deep convolutional neural networks

A review of surface ozone background levels and trends

In this paper we propose methods for estimating heterogeneity in causal effects in experimental and observational studies and for conducting hypothesis tests about the magnitude of differences in treatment effects across subsets of the population. We provide a data-driven approach to partition the data into subpopulations that differ in the magnitude of their treatment effects. The approach enables the construction of valid confidence intervals for treatment effects, even with many covariates relative to the sample size, and without “sparsity” assumptions. We propose an “honest” approach to estimation, whereby one sample is used to construct the partition and another to estimate treatment effects for each subpopulation. Our approach builds on regression tree methods, modified to optimize for goodness of fit in treatment effects and to account for honest estimation. Our model selection criterion anticipates that bias will be eliminated by honest estimation and also accounts for the effect of making additional splits on the variance of treatment effect estimates within each subpopulation. We address the challenge that the “ground truth” for a causal effect is not observed for any individual unit, so that standard approaches to cross-validation must be modified. Through a simulation study, we show that for our preferred method honest estimation results in nominal coverage for 90% confidence intervals, whereas coverage ranges between 74% and 84% for nonhonest approaches. Honest estimation requires estimating the model with a smaller sample size; the cost in terms of mean squared error of treatment effects for our preferred method ranges between 7–22%.

https://www.pnas.org/content/pnas/113/27/7353.full.pdf

Recursive partitioning for heterogeneous causal effects

Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences

Neural network modelling and prediction of hourly NOx and NO2 concentrations in urban air in London

Statistical surface ozone models: an improved methodology to account for non-linear behaviour

Meteorologically adjusted trends in UK daily maximum surface ozone concentrations

Randomized controlled trials play an important role in how Internet companies predict the impact of policy decisions and product changes. In these `digital experiments', different units (people, devices, products) respond differently to the treatment. This article presents a fast and scalable Bayesian nonparametric analysis of such heterogeneous treatment effects and their measurement in relation to observable covariates. New results and algorithms are provided for quantifying the uncertainty associated with treatment effect measurement via both linear projections and nonlinear regression trees (CART and Random Forests). For linear projections, our inference strategy leads to results that are mostly in agreement with those from the frequentist literature. We find that linear regression adjustment of treatment effect averages (i.e., post-stratification) can provide some variance reduction, but that this reduction will be vanishingly small in the low-signal and large-sample setting of digital experiments. For regression trees, we provide uncertainty quantification for the machine learning algorithms that are commonly applied in tree-fitting. We argue that practitioners should look to ensembles of trees (forests) rather than individual trees in their analysis. The ideas are applied on and illustrated through an example experiment involving 21 million unique users of this http URL.

M.W. Gardner

Papers

Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences

Neural network modelling and prediction of hourly NOx and NO2 concentrations in urban air in London

Statistical surface ozone models: an improved methodology to account for non-linear behaviour

Meteorologically adjusted trends in UK daily maximum surface ozone concentrations

A nonparametric Bayesian analysis of heterogeneous treatment effects in digital experimentation